*3.3. The PSB Technique*

The appliance dataset helps to detect one appliance by execution but might result in a problem, since more than one appliance may be ON during a certain period of time. In such a case, the active power would contain the aggregated value. Therefore, in order to decompose the consumption of each individual load, it is necessary to know how many loads are operating at that time.

Thus, considering the existence of an algorithm to classify the appliances that generate a power event, the set of instances and the appliance class create a new key-pair value. Therefore, a set of instances may contain several classes associated, and the aggregated set of instances can be decomposed considering the type of appliances.

To obtain a more accurate value of load energy consumption, the PSB takes the mean value of each block associated with each class to evaluate the mean active power during that period. Blocks that contain more than one active appliance should use the historical average value from each of them. This procedure is called disaggregation by blocks.

The load identification algorithm uses classification attributes extracted from the difference between two scenarios: before and after an appliance is turned ON. Hence, the step is just a divider between stable states. This guarantees that classifiers represent the appliances altogether, mitigating the noise generated in the transition state.

Subtracting the waveforms of these steps creates an approximation of the load waveform. Then, the CPT algorithm generates the attributes by processing the waveform. Adopting this approach, four features represent the appliances: active power, power factor, reactive factor, and non-linearity factor. Using these elements as attributes in a four-dimensional space, each load will result in a cluster. Therefore, the classification algorithms, such as KNN, can be used to set the frontiers of each load in the space.

The diagram from Figure 5 shows the state-machine algorithm with the appliance disaggregation dataset from [15]. The existence of appliance power signatures can be observed, which filter and can make the appliance disaggregation more accurate. Moreover, the methodology has algorithms for handling the ON and OFF events, presented in the diagrams from Figures 6 and 7. The methodology stores the fifteen previous cycles (0.25 s of total samples) of voltage and current waveforms to detect the appliance events if there are two or more appliances turned ON.

**Figure 5.** State-machine algorithm of the PSB.


**Figure 6.** Event ON Trigger.


**Figure 7.** Event OFF Trigger.
